[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127227-en":3,"doc-seo-127227-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127227,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",6,"Technology","Machine Learning System and Method for Predicting Risk of Drilling Component Failure - read online free","The invention addresses drilling tool failure risk assessment in the oil and gas industry, where component failures can cause major financial losses, operational downtime, and safety hazards. It predicts and helps prevent failures by combining data collection and preprocessing with feature identification and machine-learning model development. Validation uses separate historical data, while risk assessment identifies failure modes and evaluates associated risks, reinforced by partial dependence analysis and expert knowledge integration.","Technical Disclosure Commons  \nDefensive Publications Series  \n04 May 2025  \nMachine Learning System and Method for Predicting Risk of Drilling Component Failure  \nBaker Hughes Company  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nBaker Hughes Company, \"Machine Learning System and Method for Predicting Risk of Drilling Component Failure\", Technical Disclosure Commons,(May 04, 2025)  \n[https://www.tdcommons.org/dpubs_series/8073](https://www.tdcommons.org/dpubs_series/8073)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \n510641  \nTitle: Machine Learning System and Method for Predicting Risk of Drilling Component Failure  \nKeywords: Drilling Tool Failure, Risk Assessment, Machine Learning, Predictive Analytics, and Expert Knowledge  \n1. Abstract  \nThe invention addresses the problem of drilling tool failure risk assessment in the oil and gas industry. Tool failure in drilling operations can lead to significant financial losses, downtime, and safety risks. The invention aims to provide a solution for predicting and preventing these failures, which is crucial for maintaining safe and efficient drilling operations.  \nThe invention proposes a solution by combining data-driven techniques with expert knowledge. It begins with data collection, including historical data on tool performance, and involves data cleaning and pre-processing. Relevant features are identified, and a predictive model is developed using machine learning techniques. The model is validated with separate historical data, and risk assessment is performed to identify potential failure modes and assess associated risks. The integration of expert knowledge ensures that the model is accurate and relevant to domain experts, and partial dependence analysis helps evaluate parameter interactions and contributions to failure events. The use of boosting techniques and Bayesian optimization further enhances model performance.  \nThe unique aspect of this invention is its approach to combining data-driven techniques with expert knowledge during the model-building process. It proposes a comprehensive descriptive and predictive analytic framework that incorporates a wide range of drilling parameters and expert evaluation. This integration of domain expertise distinguishes it from purely data-driven models. The invention also demonstrates how this approach can improve model performance and reduce the failure rate, which is a novel and commercially significant aspect.  \nThe technical advantage of this invention over previous solutions is the integration of expert knowledge, which results in a more accurate and comprehensive risk assessment. By combining datadriven techniques with domain expertise, the model becomes more transparent, explainable, and tailored to the specific needs of the drilling operation. The inclusion of partial dependence analysis also provides insights into parameter interactions. Commercially, this approach can lead to cost savings, reduced downtime, and improved operational efficiency.  \n2. Introduction  \nDrilling is a critical process in the oil and gas industry, and it involves significant risks and challenges that can lead to failures and accidents. Tool failure risk assessment is particularly important because of the complex and often high-risk nature of drilling operations. Drill string components, downhole tools, and other drilling equipment are subject to extreme conditions such as high pressure, high temperature, and abrasive formation, which can increase the likelihood of failure.  \nTool failure risk assessment in drilling typically involves identifying potential failure modes of drilling tools and equipment","cbCaimJkiJsnoZU3","https://ap.wps.com/l/cbCaimJkiJsnoZU3","pdf",1454281,2,1,17,"English","en",105,"# Abstract\n# Introduction\n## Drilling tool failure risks and failure modes\n## Risk mitigation strategies and tool selection\n## Predictive modeling with data analytics and machine learning\n## Combining expert judgment with data-driven methods","[{\"question\":\"What problem does the invention target in drilling operations?\",\"answer\":\"It targets risk assessment for drilling tool failures, aiming to predict and prevent failures that can lead to financial losses, downtime, and safety risks.\"},{\"question\":\"How does the method build the predictive risk model?\",\"answer\":\"It combines data-driven processing—data collection, cleaning, preprocessing, feature identification, model training, and validation—with machine-learning techniques for predictive analytics.\"},{\"question\":\"Why is expert knowledge included, and what benefit does it provide?\",\"answer\":\"Expert knowledge is integrated during model building to improve accuracy and relevance to domain experts, making the assessment more comprehensive and explainable, including insights from partial dependence analysis.\"}]","Machine Learning System and Method for Predicting Risk of Drilling Component Failure - 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